Google: Machine Learning Can Be Used To Make New Ranking Signals Out Of Old Ones

A month ago, I interview Gary Illyes at Marketing Land and covered this bit at Search Engine Land but it got lost. In short, Gary Illyes from Google said that machine learning and artificial intelligence within the search algorithm can be used to make new ranking signals.

He said that Google can use it to say if you combine ranking signal A with ranking signal B, we can make a new ranking signal C that helps improve quality of the search results.

Here is that transcript bit:

Danny Sullivan: Yeah. What is it, what are you doing with machine learning? Like, so when you say it’s not being used in the core algorithm. So no one’s getting fired. The machines haven’t taken over the algorithm, you guys are still using an algorithm. You still have people trying to figure out the best way to process signals, and then what do you do with the machine learning; is [it] part of that?

Gary Illyes: They are typically used for coming up with new signals and signal aggregations. So basically, let’s say that this is a random example and not know if this is real, but let’s say that I would want to see if combining PageRank with Panda and whatever else, I don’t know, token frequency.

If combining those three in some way would result in better ranking, and for that for example, we could easily use machine learning. And then create the new composite signal. That would be one example.

The other example would be RankBrain, where… which re-ranks based on based on historical signals.

But that also is, if you, if you think about it, it’s also a composite signal.

It’s using several signals to come up with a new multiplier for the results that are already ranked by the core algorithm.

Here is the audio for that part of the interview:

Gary did not say if Google does do this now but said they can do this. Are they? What do you think?